U.S. patent number 11,308,545 [Application Number 16/159,428] was granted by the patent office on 2022-04-19 for automated order troubleshooting.
This patent grant is currently assigned to ACCENTURE GLOBAL SOLUTIONS LIMITED. The grantee listed for this patent is ACCENTURE GLOBAL SOLUTIONS LIMITED. Invention is credited to Colin Connors, Jingyun Fan, Chung-Sheng Li, Danielle Moffat, Kayhan Moharreri, Emmanuel Munguia Tapia.
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United States Patent |
11,308,545 |
Li , et al. |
April 19, 2022 |
Automated order troubleshooting
Abstract
Examples of automated order troubleshooting are described. In an
example embodiment, sales-specific data sources associated with at
least one of a process, an organization, and an industry relevant
for sales operations are monitored. From the monitored
sales-specific data, an operation behavioral pattern is identified,
based on predefined rules. Subsequently, a behavior model capturing
the operation behavioral pattern is constructed using a
pre-existing behavior model library. Using the behavior model, a
potential event relating to an order received to be fulfilled using
the sales operation is predicted, the potential event being
indicative of an issue affecting the order. Accordingly, the issue
affecting the order is proactively remediated to automatically
troubleshoot the order.
Inventors: |
Li; Chung-Sheng (San Jose,
CA), Munguia Tapia; Emmanuel (San Jose, CA), Fan;
Jingyun (Berkeley, CA), Moffat; Danielle (Highlands
Ranch, CO), Connors; Colin (Campbell, CA), Moharreri;
Kayhan (San Jose, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
ACCENTURE GLOBAL SOLUTIONS LIMITED |
Dublin |
N/A |
IE |
|
|
Assignee: |
ACCENTURE GLOBAL SOLUTIONS
LIMITED (Dublin, IE)
|
Family
ID: |
1000006247426 |
Appl.
No.: |
16/159,428 |
Filed: |
October 12, 2018 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20200118195 A1 |
Apr 16, 2020 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
10/0635 (20130101); G06Q 30/0635 (20130101); G06N
20/00 (20190101); G06Q 10/087 (20130101) |
Current International
Class: |
G06N
20/00 (20190101); G06Q 30/06 (20120101); G06Q
10/06 (20120101); G06Q 10/08 (20120101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Primary Examiner: White; Dylan C
Attorney, Agent or Firm: Mannava & Kang, P.C.
Claims
What is claimed is:
1. An Order Troubleshooting Automation (OTA) system comprising: a
processor; and a memory storing instructions, which when executed
by the processor, cause the system to: query online sales-specific
data sources associated with at least one of a process, an
organization, and an industry relevant for sales operations by
accessing at least one of a social media website, an online web
portal, and webpage associated with at least one of the process,
the organization, and the industry relevant for the sales
operations; monitor the queried sales-specific data sources in
real-time and retrieve sales-specific data from the queried
sales-specific data sources; identify an operation behavioral
pattern from the sales-specific data, based on predefined rules,
the predefined rules configured based on the at least one of the
process, the organization, and the industry, wherein the operation
behavioral pattern indicates current and uture trends in the at
least one of the process, the organization, and the industry,
wherein, to identify the operation behavioral pattern, the
processor is configured to perform an analysis on resource
requirements of at least one of the process, the organization, and
the industry by determining whether the at least one of the
process, the organization, and the industry is sensitive to an one
or a combination of latency, throughput, computational power, and
memory, wherein the determination is performed based on
characterization of workload of the at least one of the process,
the organization, and the industry based on computational
intensiveness and communication intensiveness of the workload; upon
identification of the operation behavioral pattern, construct a
behavior model capturing the operation behavioral pattern using a
pre-existing behavior model library and the sales-specific data,
wherein pre-existing behavior model library includes a template for
generating the behavior model by capturing the operation behavioral
pattern; receive, from a client, a request pertaining to an order
for a service or a product; deploy the behavior model to predict a
potential event relating to the order received to be fulfilled
using the sales operation, based on the behavior model, wherein the
potential event is predicted by tracking one or more activities
associated with the sales operation, wherein the potential event is
indicative of an issue affecting the order, wherein, to predict the
potential event, the processor is configured to: determine whether
the order conforms to the behavior model based on an operation
behavioral pattern associated with the order by determining whether
the order falls within a set of predetermined operation behavior
patterns associated with the behavior model; in response to the
determination that the order does not conform to the behavior
model: predict the potential event relating to the order to be
fulfilled using the sales operation; and calibrate the behavior
model by incorporating the operation behavioral pattern associated
with the order into the behavior model; and upon the prediction of
the potential event, determine adjustments in resource requirements
for the order based on the calibrated behavior model; and wherein
the order is modified according to the determined adjustments in
the resource requirements to automatically remediate the issue
affecting the order, thereby facilitating automatically
troubleshooting of the order in an effective manner.
2. The OTA system as claimed in claim 1, wherein the processor is
to: generate a hypothesis based on historical data; and provide a
recommendation based on the hypothesis to remediate the issue.
3. The OTA system as claimed in claim 1, wherein the processor is
to capture a plurality of steps of the sales operation, each of the
plurality of steps being identified as an issue-causing juncture in
the sales operation.
4. The OTA system as claimed in claim 1, wherein the processor is
to trace the sales operation from order placement to final delivery
to predict the potential event.
5. The OTA system as claimed in claim 1, wherein the processor is
to: investigate the order to determine whether the order conforms
to the behavioral model or not, based on the operation behavioral
pattern associated with the order; generate a hypothesis for the
order, when the order does not conform to the behavioral model; and
incorporate the hypothesis in the behavioral model to predict the
potential event.
6. The OTA system as claimed in claim 1, wherein the processor is
to generate an alert for stakeholders in the sales operation, in
response to the prediction of the potential event.
7. A method performed by a processor, the method comprising:
querying sales-specific data sources associated with at least one
of a process, an organization, and an industry relevant for sales
operations, wherein the online querying includes accessing at least
one of a social media website, and online web portal, and webpage
associated with at least one of the process, the organization, and
the industry relevant for the sales operations; monitoring the
queried sales-specific data sources in real-time and retrieving
sales-specific data from the queried. sales-specific data sources;
identifying an operation behavioral pattern from the sales-specific
data, based on predefined rules, the predefined rules configured
based on the at least one of the process, the organization, and the
industry. wherein the operation behavioral pattern indicates
current and future trends in the at least one of the process, the
organization, and the industry, wherein the identifying comprises
performing an analysis on resource requirements of at least one of
the process, the organization, and the industry by determining
whether the at least one of the process, the organization, and the
industry is sensitive to any one or a combination of latency,
throughput, computational power, and memory, wherein the
determination is performed based on characterization of workload of
the at least one of the process, the organization, and the industry
based on computational intensiveness and communication
intensiveness of the workload; upon identification of the operation
behavioral pattern, constructing a behavior model capturing the
operation behavioral pattern using a pre-existing behavior model
library and the sales-specific data, wherein pre-existing behavior
model library includes a template for generating the behavior model
by capturing the operation behavioral pattern; receiving, from a
client, a request pertaining to an order for a service or a
product; predicting a potential event relating to the order
received to be fulfilled using the sales operation, based on the
behavior model, wherein the potential event is predicted by
tracking one or more activities associated with the sales
operation, wherein the potential event is indicative of an issue
affecting the order, wherein the predicting the potential event
comprises: determining whether the order conforms to the behavior
model based on an operation behavioral pattern associated with the
order by determining whether the order falls within a set of
predetermined operation behavior patterns associated with the
behavior model; in response to the determination that the order
does not conform to the behavior model: predicting the potential
event relating to the order to be fulfilled using the sales
operation; and calibrating the behavior model by incorporating the
operation behavioral pattern associated with the order no the
behavior model, and upon the prediction of the potential event,
determining adjustments in resource requirements for the order
based on the calibrated behavior model, wherein the order is
modified according to the determined adjustments in the resource
requirements to automatically remediate the issue affecting the
order, thereby facilitating automatically troubleshooting of the
order in an effective manner.
8. The method as claimed in claim 7, wherein the remediating
comprises: generating a hypothesis based on historical data; and
providing a recommendation based on the hypothesis to remediate the
issue.
9. The method as claimed in claim 7, wherein the behavior model
captures a plurality of steps of the sales operation, each of the
plurality of steps identified as an issue-causing juncture in the
sales operation.
10. The method as claimed in claim 7, wherein the behavior model
traces the sales operation front order placement to final delivery
to predict the potential event.
11. The method as claimed in claim 7, wherein the predicting
comprises: investigating the order to determine whether the order
conforms to the behavioral model or not, based on the operation
behavioral pattern associated with the order; generating a
hypothesis for the order, when the order does not conthrm to the
behavioral model; and incorporating the hypothesis in the
behavioral model to predict the potential event.
12. The method as claimed in claim 7, further comprising generating
an alert for stakeholders in the sales operation, in response to
the prediction of the potential event.
13. A non-transitory computer readable medium including machine
readable instructions that are executable by a processor to: query
online sales-specific data sources associated with at least one of
a process, an organization, and an industry relevant for sales
operations by accessing at least one of a social media website, an
online web portal, and webpage associated with at least one of the
process, the organization, and the industry relevant for the sales
operations; monitor the queried sales-specific data sources in
real-time and retrieve sales-specific data from the queried
sales-specific data sources; identify an operation behavioral
pattern from the sales-specific data, based on predefined rules,
the predefined rules configured based on the at least one of the
process, the organization, and the industry, wherein the operation
behavioral pattern indicates current and future trends in the at
least one of the process, the organization, and the industry,
wherein, to identify the operation behavioral pattern, the
processor is configured to perform an analysis on resource
requirements of at least one of the process, the organization,and
the industry by determining whether the at least one of the
process, the organization, and the industry is sensitive to any one
or a combination of latency, throughput, computational power, and
memory, wherein the determination is performed based on
characterization of workload of the at least one of the process,
the organization, and the industry based on computational
intensiveness and communication intensiveness of the workload; upon
identification of the operation behavioral pattern, construct a
behavior model capturing the operation behavioral pattern using a
pre-existing behavior model library and the sales-specific data,
wherein pre-existing behavior model library includes a template for
generating the behavior model by capturing the operation behavioral
pattern; receive, from a client, a request pertaining to an order
for a service or a product; predict a potential event relating to
an order received to be fulfilled using the sales operation, based
on the behavior model, wherein the potential event is predicted by
tracking one or more activities associated with the sales
operation, wherein the potential event is indicative of an issue
affecting the order, wherein, to predict the potential event, the
processor is configured to: determine whether the order conforms to
the behavior model based on an operation behavioral pattern
associated with the order by determining whether the order falls
within a set of predetermined operation behavior patterns
associated with the behavior model; in response to the
determination that the order does not conform to the behavior
model: predict the potential event relating to the order to be
fulfilled using the sales operation; and calibrate the behavior
model by incorporating the operation behavioral pattern associated
with the order into the behavior model; and upon the prediction of
the potential event, determine adjustments in resource requirements
for the order based on the calibrated behavior model, wherein the
order is modified according to the determined adjustments in the
resource requirements to automatically remediate the issue
affecting the order, thereby facilitating automatically
troubleshooting of the order in an effective manner.
14. The non-transitory computer readable medium as claimed in claim
13, wherein the processor is to: generate a hypothesis based on
historical data; and provide a recommendation based on the
hypothesis to remediate the issue.
15. The non-transitory computer readable medium as claimed in claim
13, wherein the processor is to: investigate the order to determine
whether the order conforms to the behavioral model or not, based on
the operation behavioral pattern associated with the order;
generate a hypothesis for the order, when the order does not
conform to the behavioral model; and incorporate the hypothesis in
the behavioral model to predict the potential event.
16. The non-transitory computer readable medium as claimed in claim
13, wherein the processor is to: trace the sales operation from
order placement to final delivery to predict the potential event;
and capture a plurality of steps of the sales operation, each of
the phirality of steps identified as an issue-causing juncture in
the sales operation.
17. The non-transitory computer readable medium as claimed in claim
13, wherein the processor is to generate an alert for stakeholders
in the sales operation, in response to the prediction of the
potential event.
Description
BACKGROUND
In sales operations, after an order has been received, a number of
steps are executed to process the order. The last step of the
process is order servicing to execute delivery of the order.
Usually, for servicing an order, for example, in the finance
industry or in the network services industry, a lengthy process has
to be followed. For example, to service the order, the order is
first validated in terms of legitimacy of the order to determine
whether the order can be fulfilled in the first place. For
instance, a credit validation or a payment validation of the client
may be carried out to validate the order. Once validated, the order
can be routed to the penultimate step of order preparation, i.e.,
for assembly of the ordered product or for provisioning for the
ordered service, as the case may be. Subsequently, the order is
delivered to a client either by shipping, in case of the order
being for a product, or by activation, in case of the order being
for a service.
As is evident, the sales operation process has multiple milestones
and each milestone is vulnerable to an error or an issue that may
affect the order. For instance, the steps in the sales operation
that involve logistics and supply chain activities may be
particularly prone to errors and exceptions that may affect the
order delivery. These errors and exceptions may include, for
example, unavailability of the product or the occurrence of an
event which may cause delay or cancellation of the order. However,
the exceptions are usually detectable when the process reaches the
milestone, which is affected by the exception, thereby leaving
little room and time for managing the exception in an effective
manner. Consequently, the order is unable to be fulfilled, and such
order handling and management may result in client dissatisfaction
and may adversely affect business.
In addition, order management issues may also arise at the end of
the client. As an example, the client may also not be have a
pragmatic view while placing their order, and therefore, the client
may be unable to place an order that would adequately fulfill the
client's requirements. In the example, while the client may be able
to understand internal workflows and technological requirements,
the client may not be able to predict external factors, such as
evolution in the technology employed by the client, which can
potentially affect the client's order as well as the client's
requirements.
For instance, a company may order a certain kind of hardware and
infrastructure keeping in mind a present technological and economic
scenario, but does not anticipate or predict an economic decline.
Accordingly, the company may place an order which might fulfill an
immediate requirement, but eventually is not in the interest of the
company. In case the company has a substantially large market
share, such an ill-conceived order planning can have an
industry-wide ripple effect. Accordingly, such external factors can
affect the ordering decisions of the client. However, in the
absence of the knowledge of such external factors, the client may
not be in a position to make such an informed decision regarding
the order. The same scenario is equally relevant from the service
provider or the seller point of view. In the absence of pragmatic
knowledge, the order from a downstream client can have a
considerable effect on the seller or the service provider, as the
case may be. This presents a technical problem wherein the existing
infrastructure used for managing the last leg of the order may be
unable to do so in an effective or an efficient manner.
BRIEF DESCRIPTION OF DRAWINGS
Features of the present disclosure are illustrated by way of
examples shown in the following figures. In the following figures,
like numerals indicate like elements, in which:
FIG. 1 illustrates a block diagram schematic of an Order
Trouble-shooting Automation (OTA) system for providing automated
order troubleshooting, according to an example embodiment of the
present disclosure;
FIG. 2A, FIG. 2B, and FIG. 2C illustrate examples of use of the OTA
system for the purposes of providing automated order
troubleshooting, according to an example embodiment of the present
disclosure;
FIG. 3 illustrates behavior model creation and deployment by the
OTA system, according to an example embodiment of the present
disclosure;
FIG. 4 illustrates workload behavior patterns of FIG. 3, according
to an example embodiment of the present disclosure;
FIG. 5 illustrates characterization of workloads of FIG. 4,
according to an example embodiment of the present disclosure;
FIG. 6 illustrates characterization of workloads of FIG. 4,
according to an other example embodiment of the present
disclosure;
FIG. 7 illustrates an example of characterization of workloads,
according to an example embodiment of the present disclosure;
FIG. 8 illustrates operation of an issue predictor of the OTA
system, according to an example embodiment of the present
disclosure;
FIG. 9 illustrates operation of a resolver of the OTA system, in
accordance with an example embodiment of the present
disclosure;
FIG. 10 illustrates a hardware platform for implementation of the
OTA system, according to an example embodiment of the present
disclosure;
FIG. 11 illustrates a method of automated order troubleshooting,
according to an example embodiment of the present disclosure.
DETAILED DESCRIPTION
For simplicity and illustrative purposes, the present disclosure is
described by referring mainly to examples thereof. The examples of
the present disclosure described herein may be used together in
different combinations. In the following description, details are
set forth in order to provide an understanding of the present
disclosure. It will be readily apparent however, that the present
disclosure may be practiced without limitation to all these
details. Also, throughout the present disclosure, the terms "a" and
"an" are intended to denote at least one of a particular element.
As used herein, the term "includes" means includes but not limited
to, the term "including" means including but not limited to. The
term "based on" means based at least in part on, the term "based
upon" means based at least in part upon, and the term "such as"
means such as but not limited to.
The present disclosure describes aspects relating to automated
order troubleshooting. In one example, the aspects described in the
present disclosure facilitate in either handling the order after
the order has been placed by a client and received at the service
provider, or in placing the order in the first place, in a manner
that addresses the latent requirements and factors that are not
evidently perceivable. For purposes of this disclosure, the term
client is used to indicate any party that is placing orders,
whereas the term service providers has been used to indicate any
party that sells, manufactures, or otherwise provides goods,
products, and services to clients.
From the client's point of view, the techniques of the present
disclosure provide a customized order, which adequately meets their
requirements, while taking into account industry trends and
practices. At the same time, for instance, any social, economic, or
political situation that can affect the business, the industry
trends, and therefore, the order, are considered while placing the
order. In addition, the present disclosure provisions the same from
the service provider's point of view. For instance, given the
industry norms, the service provider may be able to effectively
cater to the client requirements, keeping in mind the various
factors, past, present, and future, that can affect the fulfillment
of the order or affect the demand which gave rise to the order.
To begin with, as an example, the techniques for automated order
troubleshooting are manifested in two stages, from an
implementation perspective. First, a behavior model which captures
operation behavioral pattern indicative of sales-specific behavior
associated with a process, and organization and industry, or a
combination thereof. In other words, the behavior model captures
operation behavioral pattern that is followed by clients in
relation to a specific process, or by the client organization, or
by the industry that the client belongs to. In the second stage
then, the behavioral model is deployed when an order is placed or
received, as the case may be. The behavior model is deployed to
gauge the past, present, and the future scenarios, for determining
whether the order can be affected or not, and if it can be
affected, automatic troubleshooting for the order is triggered in
order to mitigate or completely prevent the order from being
affected.
In operation, as part of the model construction, sales-specific
data sources associated with a process, an organization, an
industry, or a combination thereof relevant for sales operations
can be monitored. In an example, the process, the organization, and
the industry that is relevant to the sales operation can be
selected based on the operation of the party implementing the
techniques of the present disclosure. For the purposes of this
disclosure, the term "sales operation" intends to cover the sales
operations conducted by a service provider in a manner of selling
the product and the services as well as the operation by a client
in a manner of placing orders for the products and services offered
by various service providers, sellers, and manufacturers.
For instance, in case the party implementing the automated order
troubleshooting technique of the present disclosure is the service
provider, then the process or the organization or the industry can
be related to either the client that the service provider caters to
or the industry or field that the service provider operates in. On
the other hand, for instance, if the party implementing the
automated order troubleshooting is a client, then the monitoring of
the process, the organization, and the industry can be related to
the industry or field that the client operates in or the standard
industry-specific processes that the client would implement. The
monitoring of the sales operations can be achieved, in real-time,
by querying various data bases and repositories of information,
online as well as offline, from which the relevant data can be
retrieved. For example, social media, online web portals, and other
websites that carry information regarding the process, the
organization, the industry, or a combination thereof can be crawled
for retrieving and monitoring the above mentioned information.
Once the sales-specific data has been retrieved and stored, an
operation behavioral pattern can be identified based on the
sales-specific data. In an example, the behavioral pattern so
identified can indicate, based on the sales-specific data, the
current and future trends that can affect, for instance, demand and
supply in the industry, and difference in the current trends from
previous trends. The operation behavioral pattern can be identified
using the sales-specific data based on predefined rules, the
predefined rules mirroring the process, the organization, and the
industry relevant for the party conducting the automated order
troubleshooting. In an example, the predefined rules can be based
on historically recorded cases for that ordering process in that
industry, process, organization, and the like.
With the operation behavioral pattern ascertained, the behavior
model is then constructed to emulate the monitored sales-specific
data as well as the operation behavior pattern as mentioned above.
In an example, a pre-existing behavior model library can be the
basis for constructing the behavior model. For instance, the
pre-existing behavior model library can serve as a template for
generating the behavior model which attempts to mimic the operation
behavioral pattern. According to an aspect, the behavior model
attempts to capture various steps of the sales operation, in which
each step has been identified as an issue-causing juncture in the
sales operation. In other words, the behavior model attempts to
capture as many as possible points of failure in the
sales-to-delivery process in order management. At the same time,
the behavior model traces the sales operation from order placement
to final delivery for achieving the capability of predicting the
potential event. This concludes first phase of automated order
troubleshooting in with the construction of the behavior model.
As mentioned previously, in the second phase, the behavior model is
implemented for order management, and for automatically
troubleshooting orders, where necessary. The automation of
troubleshooting implies that there is no human intervention at any
stage, and the process of order handling, management, and
troubleshooting is machine-executed. In the second phase, the
behavior model and the associated processes are triggered as soon
as an order is placed.
As part of deployment of the behavior model, any potential event
relating to the order received to be fulfilled using the sales
operation is predicted based on the behavior model. The potential
event is indicative of an issue that can affect the order directly
or indirectly. For example, a direct influence can be in the form
of a real and present event, such as a natural calamity, that can
affect the order fulfilment. An indirect influence can be in the
form of an economic, political, or social situation brewing in a
region which may affect demand or supply, in turn, potentially
affecting the order. In addition, as mentioned previously, the
prediction of the order being affected can be from the point of
view of the service provider or of the consumer. In either case,
the factors may be same or similar. As mentioned previously, the
behavior model is built to trace the sales operation from order
placement to final delivery to predict the potential event.
In an example, as part of the prediction of the potential event, an
investigation of previously encountered exceptions during sales
process and previously raised issues during sales process may also
be done to identify the potential event or issue affecting the
order. Further, the present disclosure provides aggregation of
newfound influencers in the behavioral model. Accordingly, in one
example, the order is investigated to determine whether the order
conforms to the behavioral model or not, which, as mentioned above,
is done on the basis of the operation behavioral pattern associated
with that order. In the eventuality that the order does not conform
to the behavioral model, a hypothesis is generated for that order
and the hypothesis incorporated in the behavioral model, so as to
enable prediction of the potential event even in such an
eventuality. In other words, in case the order does not conform to
the behavior model, which means that the behavior model is unable
to accurately mirror the operation pattern, then this new
behavioral pattern is incorporated into the behavior model.
In case an event that can potentially affect the order is
predicted, automatic troubleshooting of the order is initiated and
proactive remediation of the issue affecting the order is achieved.
For example, in one case, a hypothesis is generated based on
historical data and a recommendation for resolving the issue is
provided based on the hypothesis. In addition, as part of
automation of order troubleshooting, an alert can be generated for
all stakeholders, such as sales team, operations team, delivery
teams, and validation teams, in the sales operation, in response to
the prediction of the potential event, which in turn indicates the
issue affecting the order. This may allow the stakeholders to
pre-emptively provide a resolution to the identified issue.
FIG. 1 illustrates an order troubleshooting automation (OTA) system
100 for providing automated order troubleshooting, according to an
example embodiment of the present disclosure. In an example
embodiment, the OTA system 100 facilitates in either handling the
order after the order has been placed by a client and received at
the service provider, or in placing the order in the first place,
in a manner that it addresses the latent requirements and factors
that are not evidently perceivable. Accordingly the OTA system 100
may be deployed at either the client end or at the service provider
end. As mentioned previously also, though the term sales operation
has been used previously as well as henceforth, it will be
understood that the term means to cover the sales operations
conducted by a service provider in a manner of selling the product
and the services as well as the operation by a client in a manner
of placing orders for the products and services offered by various
service providers, sellers, and manufacturers.
At the client end, in an example, the OTA system 100 may provide a
tailor-fitted order, which adequately meets their requirements,
while taking into account industry trends and practices. At the
same time, the OTA system 100 may also consider external, possibly
hidden factors, for instance, any social, economic, or political
situation, that can affect the business, the industry trends, the
market demand, and therefore, the order, while placing the order.
In addition, the OTA system 100 may provide a similar facility when
implemented at the service provider's end. For instance, the OTA
system 100 may be able to take into account the industry norms and
various other factors that can affect the fulfilment of the order
or affect the demand, which gave, rise to the order in the first
place, to allow the service provider to effectively cater to the
client requirements.
As part of achieving the above function, the OTA system 100 can
include a behavior model constructor 102 and an issue predictor
104. The behavior model 102 constructs a behavior model that can
mirror the various factors that may influence an order, whereas the
issue predictor 104 can use the behavior model and provide
automated order troubleshooting. In addition, the issue predictor
104 may also include provisions for improving the behavior model in
cases where the behavior model is unable to cater to a situation.
The behavior model constructor 102 can include a pattern identifier
106 and a modellor 108 to accomplish the above mentioned function
of the behavior model constructor 102. Further, the issue predictor
104 includes a model deployer 110, a resolver 112, and a model
augmentor 114 to facilitate the issue predictor 104 in predicting
an issue for automatically troubleshooting an order.
Therefore, the behavior model constructor 102 and the issue
predictor 104 can operate in two phases, from an operation
perspective. In the first phase, the behavior model constructor 102
builds a behavior model which captures operation behavioral pattern
indicative of sales-specific behavior associated with a process,
and organization and industry, or a combination thereof, associated
with the party implementing the OTA system 100. In other words, the
behavior model constructor 102 creates the behavior model to
capture operation behavioral pattern that is followed by clients in
relation to a specific process of the service provider or vice
versa, or by the implementer's organization, or by the industry
that the implementer belongs to. In the second phase then, the
issue predictor 104 deploys the behavioral model when an order is
placed or received, as the case may be. In an example, the issue
predictor 104 deploys the behavior model to gauge the past,
present, and the future scenarios, for determining whether the
order can be affected or not, and if it can be affected.
Accordingly the issue predictor 104 carries out automatic
troubleshooting for the order to mitigate or completely prevent the
order from being affected.
In operation, as part of the model construction, the pattern
identifier 102 can monitor, in real time, various sources of
information that can be relevant for maintaining a watch over the
factors that could possibly influence the completion or
non-completion of an order whether for a service provider or for a
client. Accordingly, the pattern identifier 106 monitors
sales-specific data sources associated with a process, an
organization, an industry, or a combination thereof relevant for
sales operations can be monitored. In an example, the process, the
organization, and the industry that relevant to the sales operation
can be selected based on the operation of the party implementing
the techniques of the present disclosure.
For instance, in case the implementer of the OTA system 100 is the
service provider, then the process or the organization or the
industry can be related to either the client that the service
provider caters to or the industry or field that the service
provider operates in. On the other hand, for instance, if the
implementer of the OTA system 100 is a client, then the monitoring
of the process, the organization, and the industry can be related
to the industry or field that the client operates in or the
standard industry-specific processes that the client would
implement. The pattern identifier 106 can, in real-time, query
various data bases and repositories of information, online as well
as offline, from which the relevant data can be retrieved. For
example, the pattern identifier 106 may query social media, online
web portals, and other websites that carry information regarding
the process, the organization, the industry, or a combination
thereof, which can be crawled by the pattern identifier 106 for
retrieving and monitoring the above mentioned information.
Once the sales-specific data has been retrieved and stored, the
pattern identifier 106 may identify an operation behavioral pattern
based on the sales-specific data. In an example, the behavioral
pattern so identified by the pattern identifier 106 can indicate
the current and future trends that can affect, for instance, demand
and supply in the market for goods and services relevant for an
industry, and difference in the current trends from previous
trends. The pattern identifier 106 may identify the operation
behavioral pattern using the sales-specific data based on
predefined rules which may attempt to emulate the process, the
organization, and the industry relevant for the implementer
conducting the automated order troubleshooting. In an example, the
predefined rules can be based on historically recorded cases for
that ordering process in that industry, process, organization, and
for that implementer.
With the operation behavioral pattern ascertained, the modellor 108
can then construct the behavior model to emulate the monitored
sales-specific data as well as the operation behavior pattern as
mentioned above. In an example, the modellor 108 can take into
account a pre-existing behavior model library as the basis for
constructing the behavior model. For instance, the modellor 108 can
use the pre-existing behavior model library as a template for
generating the behavior model which attempts to mimic the operation
behavioral pattern.
According to an aspect, the behavior model so constructed by the
modellor 108 traces the sales operation from order placement to
final delivery for achieving the capability of predicting the
potential event. At the same time, the modellor 108, through the
behavior model, can attempt to capture various steps of the sales
operation, in which each step has been identified as an
issue-causing juncture in the sales operation. In other words, the
behavior model can attempt to capture as many as possible points of
failure in the sales-to-delivery process in order management.
Further, as mentioned previously, subsequent to creation of the
behavior model, the issue predictor 104 implements the behavior
model created by the behavior model constructor 102 for order
management, and for automatically troubleshooting orders, where
necessary. In an example, the issue predictor 104 is triggered for
deploying the behavior model as soon as an order is placed by the
client or received by the service provider, as the case may be.
As part of deployment of the behavior model, the model deployer 110
uses the behavior model to predict any potential event relating to
the order that will be fulfilled using the sales operation. The
model deployer 110 can predict the potential event to indicate an
issue that can affect the order directly or indirectly. For
example, a direct influence can be in the form of a real and
present event, such as a natural calamity, that can affect the
order fulfilment. An indirect influence can be in the form of an
economic, political, or social situation brewing in a region which
may affect a demand or supply which, in turn, affects the
order.
In addition, as mentioned previously, the model deployer 110 can
predict the order being affected from the point of view of the
party implementing the OTA system 102, i.e., the service provider
or the consumer. In either case, the model deployer 110, in an
example, may identify that the potential event can be the same, and
may equally affect the clients as well as service providers. As
mentioned previously, the model deployer 110 uses the behavior
model to trace the sales operation from order placement to final
delivery to assess each point of failure along the way so as to
accurately predict the potential event, subsequently, its effect on
the order, and may recommend a possible resolution.
In an example, as part of the prediction of the potential event,
the model deployer 110 can conduct an investigation of previously
encountered exceptions during sales process and previously raised
issues during sales process to identify the potential event or
issue affecting the order. In addition, in the example, as part of
the investigation, the model deployer 110 may further collect
evidence in the form of historical cases. In case an event that can
potentially affect the order is predicted, the resolver 112
initiates automatic troubleshooting of the order and performs a
proactive remediation of the issue affecting the order. For
example, in one case, as part of the automated troubleshooting, the
resolver 112 may generate a hypothesis based on the historical case
data and then build a recommendation based on the hypothesis for
remediating the issue. In an example, the recommendation can be in
the form of an action item that can be performed by the implementer
of the OTA system 100 to mitigate the effects of the issue on the
order or to altogether avoid the effects.
In addition, as part of automation of order troubleshooting, the
resolver 112 can generate an alert or all stakeholders, such as
sales team, operations team, delivery teams, and validation teams,
in the sales operation, in response to the prediction of the
potential event, which may allow the stakeholders to preemptively
provide a resolution to the issue associated with the predicted
event.
Further, the OTA system 100 may also provide aggregation of
newfound influencers which were earlier either not known or not
considered, in the behavioral model. Accordingly, in one example,
the model augmentor 114 investigates the order and its conformance
to the behavioral model on the basis of the operation behavioral
pattern associated with that order. For instance, the model
augmentor 114 can determine whether the current order falls within
the category of known operation behavior patterns or is it an
outlier. In case the order does not conform to the behavior model,
it may indicate that the behavior model is unable to accurately
mirror the operation pattern, then this new behavioral pattern is
incorporated into the behavior model. In the eventuality that the
model augmentor 112 determines that the order does not conform to
the behavioral model, the model augmentor 114 further generates a
hypothesis for that order and incorporates the hypothesis in the
behavioral model, so as to enable prediction of the potential event
even in such an eventuality. In an example, the model augmentor 114
can, based on the hypothesis, generate a recommendation as to the
changes in the behavioral model which would accommodate the impact
due to the new order.
FIG. 2A, FIG. 2B, and FIG. 2C illustrate examples of the
implementation of the OTA system 100 for the purposes of providing
automated order troubleshooting, according to an example embodiment
of the present disclosure. While FIG. 2A illustrates the
implementational example of the OTA system 100 when used in the
field of products, in general, FIG. 2B illustrates the
implementational example of the OTA system 100 in the field of
electronics and high-technology products and services for use in
financial services, and FIG. 2C illustrates the implementation
example of the OTA system 100 when used in the network services
industry. For the sake of brevity and ease of understanding, FIG.
2A, FIG. 2B, as well as FIG. 2C are described in conjunction to the
extent that they have common components. As mentioned previously,
the OTA system 100 can be implemented by the clients and service
providers alike. Further, in all the figures, FIG. 2A, FIG. 2B, and
FIG. 2C, the parts of the implementational example within the
dotted box indicates the steps implemented conventionally as part
of the general order management and processing procedures, whereas
the portions of the example which fall outside the dotted lines
indicate the contribution of the OTA system 100 to the procedures
of order handling and processing, as an example. Again, for the
sake of brevity, the description below focusses on the OTA system
100 and not on the conventionally known part of the procedure.
As shown in FIGS. 2A, 2B, and 2C, the first block 200 is where the
implementer's systems capture an order or a request for a service,
or as shown in FIG. 2A, a request for replacement of a product. As
mention above, as soon as the order is captured, the issue
predictor 104 is triggered into action. Accordingly, at block 202
in FIG. 2A, 2B, and 2C, recommendations are received from the
stakeholders relevant to the implementer. For example, when the
implementer of the OTA system 100 is the client, then the
recommendation can be received as part of the industry knowledge.
On the other hand, if the implementer is the service provider, then
the recommendation is received from the client or as part of the
client segmentation knowledge.
Further, after the implementer' system has received recommendations
and validation of the orders has to be performed, for example, in
terms of the validation of inventory, or validation of bandwidth,
or validation of configuration in cases of electronic equipment, or
payment validation, the OTA system 100 kicks in at block 204 and
constructs and deploys the behavior model. FIG. 2A also shows the
monitoring performed by the pattern identifier 106 by operating
block 205. The functional and operational details with respect to
the operation in reference to blocks 204 and 205 have been
explained in detail with respect to FIG. 1.
The issue predictor 104 operates block 206 uses the model deployer
110 and the resolver 112 in order to, first, identify potential
threats to the order, and, secondly, proactively remediate the
threats to provide automated order troubleshooting in order to
ensure that the order remains unaffected or is the effects are at
least mitigated. This is performed in the manner explained
previously with respect to FIG. 1.
Further, as shown in FIG. 2A and also as explained above, the model
deployer 110 can operate block 208 conduct an investigation of
previously encountered exceptions during sales process and
previously raised issues during sales process to identify the
potential event or issue affecting the order. In addition, in the
example, as part of the investigation, the model deployer 110 may
further collect evidence in the form of historical cases 210. Block
212 is triggered in case an event that can potentially affect the
order is predicted, where the resolver 112 initiates automatic
troubleshooting of the order and performs a proactive remediation
of the issue affecting the order.
As shown at least in FIG. 2B and 2C, in addition to operation of
block 202 where industry or client recommendations are taken into
consideration, in operation of block 214, block 216, and block 218,
the OTA system 100 takes into account the industry specific
knowledge, the client specific knowledge, as well as generic
knowledge. As a result, the OTA system 100 is well-equipped to be
able to predict with considerable accuracy any factors or events
that can potentially affect the order, in terms of the industry or
client standards and requirements, as well as in terms of latent
events that the industry or the client alone are unable to capture,
foresee, or take into consideration.
For instance, even for the client requirements and standards, few
of the client standards and practices may be disclosed by the
client for the service provider to effectively service the
requests. However, there may be other specific client-end policies,
practices, and norms, such as internal workflows, environments, and
existing system configurations, that the client may not disclose
but the OTA system 100 is able to capture to provide high quality
service in terms of order fulfilment and reduction of risk for the
service provider as well as for the client. For example, the
pattern identifier 106 may determine from the monitoring that the
client is in the growth stage and, therefore, might require an
aggressive order. However, if the order that is actually received
is not aggressive enough and does not align with the requirements
of the client, therefore, the resolver 112 may recommend an
aggressive order for the client. The model deployer 110 may, in
this example, ascertain that in the absence of an aggressive order,
the client would be unable to meet the market demand and, which in
turn, would affect the paying ability of the client. Accordingly,
the resolver 112 may recommend an aggressive order keeping in mind
the interest of the client as well as of the service provider
itself.
FIG. 3 illustrates the behavior model creation and deployment 300
by the OTA system 100, according to an example embodiment of the
present disclosure. For instance, in the above example, FIG. 3
illustrates the behavior model for electronics and high-technology
for the financial service industry in continuation to the example
shown in FIG. 2B.
As shown in FIG. 3, pattern identifier 106 operates block 302 to
obtain operation behavior pattern in the form of workload behavior
patterns for the relevant industry, such as the financial services
industry, on the implementer's systems. For example, the pattern
identifier 106 can determine workload patterns on the various
system components and the variation in the workloads during day and
during night, types of workloads at different times of the day, and
expected deviation from the determined workloads. Further, the
pattern identifier 106 operates blocks 304 and 306 to perform an
analysis of the system's resource requirement which involves
determining whether the system is sensitive to latency, throughput,
computational power, memory, input-output operations, and whether
the system requires any upgrade or supplemental components to meet
the needs.
Once the pattern identifier 106 has performed the identification of
the operation behavioral pattern in the above mentioned manner, the
modellor 108 operates block 308 and 310, for instance,
simultaneously, to use a library of previously handled cases by the
OTA system 100 and a library of behavior models previously
constructed by the OTA system 100 for the ordering process for the
implementer. In addition, the modellor 108 may also take into
account the current ordering process followed by the implementer to
construct the behavior model by operating block 312.
Subsequently, the model deployer 110 operates block 314 to deploy
the constructed behavior model for the current operation behavior
pattern, after an order is placed. The resolver 112 can operate
block 316 to proactively forecast the behavior and to predict the
point of failure of the order management process. In the manner
explained above, the resolver 112 can operate blocks 318 and 320 to
generate a hypothesis based on historical data using the behavior
model, and then provide a recommendation based on the hypothesis
for remediating the issue.
In addition, the model augmentor 114 can operate the path 322 from
block 316 if the current behavior model is unable to cater to the
present order and is unable to effectively predict the issue that
can affect the order. Accordingly, in the manner explained
previously, the model augmentor 114 can investigate the
non-conformance of the order and the behavioral, generate a new
hypothesis for the order, and incorporating the hypothesis in the
behavioral model to predict the potential event. The model
augmentor 114 may then operate at blocks 308 and 310 and save the
modified behavioral model in the model library and the current case
in the case library for later use.
FIG. 4 illustrates the workload behavior patterns discussed in
block 302 in FIG. 3 for the implementer systems in the electronics
and high-technology for the financial service industry, in
accordance with an example embodiment of the present
disclosure.
In the example shown in FIG. 4, the workload behavior patterns may
include workloads 400 that are shown as divided into front-end
workloads 402 and record or backend workloads 404. For instance,
the front-end workloads 402 may include client onboarding workloads
406, client engagement personalization workloads 408, cross-selling
or up-selling workloads 410, client support workloads 412, and
virtual assistance workloads 414--all of them built into the
implementer system for catering to service providing in the
financial services industry.
Further, the backend workloads 404 may further be divided into
core-banking workloads 416 and risk, compliance, and settlement
(RCS) workloads 418. For instance, the core-banking workloads 416
may be characterized by TPC-C benchmark used for benchmarking
online transaction processing whereas the RCS workloads 418 can be
characterized by TPC-H benchmark used for benchmarking decision
support in the financial services industry.
In an example, the core-banking workloads 416 can involve
transactional processes handled by the implementer system during
the day where large number of transactions are handled by the
implementer system. Accordingly, the implementer system may require
resources which are capable of handling a large number of light
transactions and ones that are capable of quickly storing the
transaction data. Accordingly, the core-banking workloads 416 can
include online transaction processing (OLTP) workloads 420,
straight-through processing (STP) workloads 422, and financial
instruments trading workloads 424.
On the other hand, in one example, the RCS workloads 418 may
involve transactional processes that are handled by the implementer
system during the night when the settlement of the transactions has
to be completed by the implementer system. Accordingly, in the
present example, the implementer system may have to handle batch
transactions that may be fewer in number but are resource-heavy as
they may perform analytics, compliance and risk checks,
verifications, and validations of the few selected transactions
performed during the day. The implementer system, therefore, may
require computing resources in terms of processing and storage
capability. Accordingly, the RCS workloads 418 can include online
analytical processing (OLAP) workloads 426, compliance related
workloads 428, fraud detection workloads 430, and trade settlement
workloads 432.
As an example, FIG. 5 illustrates the characterization of the
various workloads 400 discussed with reference to FIG. 4 above on
the basis of various system requirements. For example, the
workloads 400 can be characterized based on synchronization
traffic, bulk data, and thread contention as shown in chart 500. In
another example, the workloads 400 may be characterized based on
data and the storage required for that data as shown in chart 502.
In the present example, the data and the storage may be based on
transaction processing and the database used, the analytics and
high-performance computing (HPC) performed, the web collaboration
and infrastructure used by the implementer system, and the business
applications that are run by the implementer system.
As another example of characterization of workloads 400, FIG. 6
illustrates a chart 600 which shows system resource usage as a
basis of workload characterization. In the present example, the
workloads 400 can be characterized based on computational
intensiveness of the workloads 400, i.e., if the workloads 400 use
high processing capability as well as high storage capacity
resources, and on the basis of the communication intensiveness,
i.e., whether the workloads 400 use high capacity input-output and
network resources. Accordingly, the workloads 400 in the
implementer system may range from email server workloads 602 which
are low on computational as well as communication intensiveness to
deep learning workloads 604 which are high on both computational
and communication intensiveness, with various other kinds of
workloads 400 in between the two, as shown in FIG. 6.
FIG. 7 illustrates, as an example embodiment of the present
disclosure, a chart 700 showing the computing resource requirements
of the workloads 400, as shown in FIG. 5 and FIG. 6, on the basis
of the industry. As will be understood, the aspects illustrated
with reference to FIG. 3, FIG. 4, FIG. 5, and FIG. 6 are
incorporated and captured by the behavior model constructor 102
while generating the behavior model. In the same manner, the
behavior model so constructed also takes into account industry
knowledge, as illustrated in FIG. 7, to accurately represent the
operation behavioral patterns relevant to a specific industry.
For example, as shown in FIG. 7, the industry-type may range from
financial industry 702, pharmaceutical industry 704, oil & gas
industry 706, and retail industry 708. In each industry, the
workloads 400 shown in FIG. 7 may be specific to every industry.
For example, in the pharmaceutical industry 704, the resource
intensive workloads may include gene sequencing workloads 710,
personal medicine workloads 712, adverse drug reaction capturing
workloads 714, and regulatory compliance workloads 716. In another
example, in the oil & gas industry 706, the resource intensive
workloads can include oil & gas exploration workloads 718 and
seismic migration workloads 720. In yet another example, in the
retail industry 708, the resource intensive workloads can include
cross-selling or up-selling recommendation workloads 722, supply
chain optimization workloads 724, post-payment audit workloads 726,
and client onboarding workloads 730.
FIG. 8 illustrates operation 800 of the issue predictor 104 using
the information captured in the behavior model constructed in
accordance with the aspects described in FIG. 3, FIG. 4, FIG. 5,
FIG. 6, and FIG. 7, according to an example embodiment of the
present disclosure. The operation of the issue predictor 104 as
described henceforth with reference to FIG. 8 may be in addition to
the operation described previously or may be as an alternative.
As mentioned previously, the issue predictor 104 is triggered once
the behavior model constructor 102 has constructed the behavior
model. Accordingly, to begin with, the model deployer 110 can
operate block 802 to identify or select an industry, which is
relevant for the deployment of the behavior model. Subsequently,
the model deployer 110 can operate block 804 and retrieve the
industry knowledge, based on the industry, from the behavior model.
For instance, the model deployer 110 can employ the information
gathered as part of the behavior model explained with reference to
FIG. 7 previously to obtain the industry-specific knowledge, say in
terms of the computational resources.
Further, the model deployer 110 may operate block 806 and retrieve
the behavior model which is relevant for the current case. For
instance, the behavior model can be identified based on the order
type, the client, the process, the organization, the industry, or a
combination thereof. In addition to the retrieval of the behavior
model, the model deployer 110 may operate block 808 to calibrate
the behavior model based on specifics associated with the client.
For instance, the model deployer 110 may calibrate the behavior
model based on the above mentioned parameters, such as order type,
the client, the process, the organization, the industry, or a
combination thereof. In another case, as part of the
re-calibration, the model augmentor 114 may update the behavior
model in case the order and the behavior model are not in
conformance with each other, in the manner as has been described
previously. Accordingly, the model augmentor 114 may take into
consideration latent requirements and influences that may otherwise
affect the order of the client which are otherwise not visible to
the client.
Subsequently, the resolver 112 may operate block 810 to determine
for adjustment the resource requirements for the current order,
based on the recalibrated behavior model. For example, block 812
illustrates the various adjustments that the resolver 112 may
determine for the current order, using the recalibrated behavior
model. Accordingly, once the various adjustments have been
determined, the resolver 112 can recommend the adjustments to the
client for troubleshooting the order.
Therefore, few of the client requirements that may be disclosed by
the client may not adequately meet the client requirements. The
above mentioned operation of the issue predictor 104 with reference
to FIG. 8 allows for the service provider to effectively service
the requests by recommending modifications in the order by
determining the adjustments in the requirements of the client.
Therefore, there may be latent factors, such as client-end
policies, practices, and norms, internal workflows, environments,
and existing system configurations, that the client may not
disclose but are relevant for the order to be appropriately catered
to. With the aspects of the OTA system 100 described in FIG. 8, the
OTA system 100 may provide for automated troubleshooting of the
order by taking into account potential issues or events that can
cause the order to be ineffective for the client. For example, with
the original order, the client may be unable to meet the market
demand and, which in turn, would affect the paying ability of the
client. Accordingly, the resolver 112 may consider this eventuality
and recommend the adjustments in the resource requirements, and
therefore, modifications in the order. At the same time, the
adjusted requirement is stored in the case library, as part of the
learning repository for use in due course by the OTA system
100.
FIG. 9 illustrates the operation 900 of the resolver 112 in
providing proactive remediation of the issue affecting the order,
in accordance with an example embodiment of the present disclosure.
The operation of the resolver 112 as described henceforth with
reference to FIG. 9 may be in addition to the operation described
previously or may be as an alternative.
To begin with, the resolver 112 may operate block 912 to
continuously track and monitor status of the order and may operate
block 914 to, simultaneous to the tracking and monitoring, employ
the re-calibrated behavior model to perform a what-if analysis on
the current status of the order. For example, if there is an
expected natural calamity or a geo-political event, or any other
event that may affect the order, the resolver 112 may identify such
an event using the behavior model, by performing the what-if
analysis to formulate various eventualities that may occur in the
wake of the event and, accordingly, operate block 906 to anticipate
the exceptions or errors that may occur due to such events. Block
908 illustrates the various exceptions or errors that the deployer
112 can identify using the behavior model, as explained above.
Further, block 910 illustrates the various troubleshooting options
that the resolver 112 can provide for troubleshooting the order and
mitigating the influence of the event and the exceptions
caused.
FIG. 10 illustrates a hardware platform 1000 for implementation of
the OTA system 100, according to an example of the present
disclosure. Particularly, computing machines such as but not
limited to internal/external server clusters, quantum computers,
desktops, laptops, smartphones, tablets and wearables which may be
used to execute the OTA system 100 or may have the structure of the
hardware platform 1000. The hardware platform 1000 may include
additional components not shown and that some of the components
described may be removed and/or modified. In another example, a
computer system with multiple GPUs can sit on external-cloud
platforms including Amazon Web Services, or internal corporate
cloud computing clusters, or organizational computing resources,
etc.
Referring to FIG. 10, the hardware platform 1000 may be a computer
system 1000 that may be used with the examples described herein.
The computer system 1000 may represent a computational platform
that includes components that may be in a server or another
computer system. The computer system 1000 may execute, by a
processor (e.g., a single or multiple processors) or other hardware
processing circuit, the methods, functions and other processes
described herein. These methods, functions and other processes may
be embodied as machine readable instructions stored on a computer
readable medium, which may be non-transitory, such as hardware
storage devices (e.g., RAM (random access memory), ROM (read only
memory), EPROM (erasable, programmable ROM), EEPROM (electrically
erasable, programmable ROM), hard drives, and flash memory).The
computer system 1000 may include a processor 1002 that executes
software instructions or code stored on a non-transitory computer
readable storage medium 1004 to perform methods of the present
disclosure. The software code includes, for example, instructions
to perform the steps described with reference to the components of
the OTA system 100 in FIG. 1 to FIG. 9 previously. In an example,
the behavior model constructor 102, the pattern identifier 106, the
modeler 108, the issue predictor 104, the model delayer 110, the
resolver 112, and the model augmentor 114 may be software codes or
components performing these steps.
The instructions on the computer readable storage medium 1004 are
read and stored the instructions in storage 1006 or in random
access memory (RAM) 1008. The storage 1006 provides a large space
for keeping static data where at least some instructions could be
stored for later execution. The stored instructions may be further
compiled to generate other representations of the instructions and
dynamically stored in the RAM 1008. The processor 1002 reads
instructions from the RAM 1008 and performs actions as
instructed.
The computer system 1000 further includes an output device 1010 to
provide at least some of the results of the execution as output
including, but not limited to, visual information to users, such as
external agents. The output device can include a display on
computing devices and virtual reality glasses. For example, the
display can be a mobile phone screen or a laptop screen. GUIs
and/or text are presented as an output on the display screen. The
computer system 1000 further includes input device 1012 to provide
a user or another device with mechanisms for entering data and/or
otherwise interact with the computer system 1000. The input device
may include, for example, a keyboard, a keypad, a mouse, or a
touchscreen. In an example, output of any component of the OTA
system 100 is displayed on the output device 1010. Each of these
output devices 1010 and input devices 1012 could be joined by one
or more additional peripherals.
A network communicator 1014 may be provided to connect the computer
system 1000 to a network and in turn to other devices connected to
the network including other clients, servers, data stores, and
interfaces, for instance. A network communicator 1014 may include,
for example, a network adapter such as a LAN adapter or a wireless
adapter. The computer system 1000 includes a data source interface
1016 to access data source 1016. A data source is an information
resource. As an example, a database of exceptions and inferencing
rules may be a data source. Moreover, knowledge repositories and
curated data may be other examples of data sources.
FIG. 11 illustrates a method 1100 of automated order
troubleshooting, according to an example embodiment of the present
disclosure. The method 1100 for automated order troubleshooting may
be implemented in two phases. In the first stage, a behavior model
which captures operation behavioral pattern indicative of
sales-specific behavior associated with client's process,
organization, industry, or a combination thereof. In the second
phase, the behavioral model is deployed when an order is placed or
received to gauge the past, present, and the future scenarios, for
determining whether the order can be affected or not, and if it can
be affected, automatic troubleshooting for the order is triggered
in order to mitigate the effects or completely prevent the order
from being affected.
Referring to the method 1100, at block 1102, sales-specific data
sources associated with a process, an organization, an industry, or
a combination thereof relevant for sales operations can be
monitored. In an example, the process, the organization, and the
industry that relevant to the sales operation can be selected based
on the operation of the party implementing the techniques of the
present disclosure. The monitoring of the sales operations can be
achieved, in realtime, by querying various data bases and
repositories of information, online as well as offline, from which
the relevant data can be retrieved. For example, social media,
online web portals, and other websites that carry information
regarding the process, the organization, the industry, or a
combination thereof can be crawled by for retrieving and monitoring
the above mentioned information.
Further, at block 1104, an operation behavioral pattern can be
identified based on the sales-specific data. In an example, the
behavioral pattern so identified can indicate, based on the
sales-specific data, the current and future trends that can affect,
for instance, demand and supply in the industry, and difference in
the current trends from previous trends. The operation behavioral
pattern can be identified using the sales-specific data based on
predefined rules, the predefined rules mirroring the process, the
organization, and the industry relevant for the party conducting
the automated order troubleshooting. In an example, the predefined
rules can be based on historically recorded cases for that ordering
process in that industry, process, organization, and the like.
Subsequently, at block 1106, the behavior model is then constructed
to emulate the monitored sales-specific data as well as the
operation behavior pattern as mentioned above. In an example, a
pre-existing behavior model library can be the basis for
constructing the behavior model. For instance, the pre-existing
behavior model library can serve as a template for generating the
behavior model which attempts to mimic the operation behavioral
pattern. According to an aspect, the behavior model attempts to
capture various steps of the sales operation, in which each step
has been identified as an issue-causing juncture in the sales
operation. In other words, the behavior model attempts to capture
as many as possible points of failure in the sales-to-delivery
process in order management. At the same time, the behavior model
traces the sales operation from order placement to final delivery
for achieving the capability of predicting the potential event.
This concludes the first phase of the method 1100 for automated
order troubleshooting. Subsequently, the second phase of the method
1100 commences.
Accordingly, at block 1108, an order is received, which can be
catered to by deploying the behavior model to automatically
troubleshoot the order, if required.
Subsequently, the order is assessed in view of the behavior model.
Accordingly, at block 1110, any potential event relating to the
order received to be fulfilled using the sales operation is
predicted based on the behavior model. The potential event is
indicative of an issue that can affect the order directly or
indirectly. For example, a direct influence can be in the form of a
real and present event, such as a natural calamity, that can affect
the order fulfilment. An indirect influence can be in the form of
an economic, political, or social situation brewing in a region
which may affect a demand or supply which, in turn, affects the
order. In addition, as mentioned previously, the prediction of the
order being affected can be from the point of view of the service
provider or of the consumer. In either case, the factors may be
same or similar. As mentioned previously, the behavior model is
built to trace the sales operation from order placement to final
delivery to predict the potential event.
In an example, as part of the prediction of the potential event, an
investigation of previously encountered exceptions during sales
process and previously raised issues during sales process may also
be done to identify the potential event or issue affecting the
order. Further, the present disclosure provides aggregation of
newfound influencers in the behavioral model. Accordingly, in one
example, the order is investigated to determine whether the order
conforms to the behavioral model or not, which, as mentioned above,
is done on the basis of the operation behavioral pattern associated
with that order. In the eventuality that the order does not conform
to the behavioral model, a hypothesis is generated for that order
and the hypothesis incorporated in the behavioral model, so as to
enable prediction of the potential event even in such an
eventuality. In other words, in case the order does not conform to
the behavior model, which means that the behavior model is unable
to accurately mirror the operation pattern, then this new
behavioral pattern is incorporated into the behavior model.
Further, at block 1112, in case an event that can potentially
affect the order is predicted, automatic troubleshooting of the
order is initiated and proactive remediation of the issue affecting
the order is achieved. For example, in one case, a hypothesis is
generated based on historical data and a recommendation for
resolving the issue is provided based on the hypothesis. In
addition, as part of automation of order troubleshooting, an alert
can be generated for all stakeholders, such as sales team,
operations team, delivery teams, and validation teams, in the sales
operation, in response to the prediction of the potential event.
This may allow the stakeholders to pre-emptively provide a
resolution to the issue associated with the predicted potential
event.
What has been described and illustrated herein are examples of the
present disclosure along with some of its variations. The terms,
descriptions and figures used herein are set forth via illustration
only and are not meant as limitations. Many variations are possible
within the spirit and scope of the subject matter, which is
intended to be defined by the following claims and their
equivalents in which all terms are meant in their broadest
reasonable sense unless otherwise indicated.
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